Deep-learning-based multilabel image annotation is receiving increasing attention in the field of remote sensing due to the great success of deep networks in single-label remote sensing image classification. Compared with those low-level features, the features extracted by the convolutional neural network (CNN) are more informative and can alleviate the problem of semantic gap. However, the CNN model tends to ignore the smaller objects when objects of different sizes exist in an image. In addition, how to efficiently leverage the correlation among multiple labels to enhance annotation performance remains an open issue. In this article, we propose an end-to-end deep learning framework for multilabel remote sensing image annotation. The framework is composed of a multiscale feature fusion module, a channel-spatial attention learning module, and a label correlation extraction module. The multiscale features from different layers of a CNN model are first fused and refined by using a channel-spatial attention mechanism. Then, the label correlation information is extracted from a label co-occurrence matrix and embedded into the multiscale attentive features to increase the discriminative ability of the resulting image features. The experiments on two benchmark datasets demonstrate the superiority of the proposed method in comparison with the state-of-the-art methods.

中文翻译：

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具有多尺度注意力和标签相关性的多标签遥感图像注释


由于深度网络在单标签遥感图像分类方面取得的巨大成功，基于深度学习的多标签图像标注在遥感领域受到越来越多的关注。与那些低级特征相比，卷积神经网络（CNN）提取的特征信息量更大，可以缓解语义差距问题。然而，当图像中存在不同尺寸的物体时，CNN模型往往会忽略较小的物体。此外，如何有效地利用多个标签之间的相关性来增强注释性能仍然是一个悬而未决的问题。在本文中，我们提出了一种用于多标签遥感图像标注的端到端深度学习框架。该框架由多尺度特征融合模块、通道空间注意力学习模块和标签相关性提取模块组成。首先使用通道空间注意力机制融合和细化来自 CNN 模型不同层的多尺度特征。然后，从标签共现矩阵中提取标签相关信息并将其嵌入到多尺度注意特征中，以增加所得图像特征的判别能力。两个基准数据集上的实验证明了所提出的方法与最先进的方法相比的优越性。